Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "325" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.005862 | -0.824335 | 0.908134 | -1.217181 | -0.045500 | -1.029718 | -1.729023 | 0.561352 | 0.5038 | 0.5080 | 0.3803 | nan | nan |
| 2459997 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.421698 | -0.995988 | 1.177704 | -1.160338 | 0.446032 | -1.128413 | -2.341866 | 1.052290 | 0.5190 | 0.5246 | 0.3833 | nan | nan |
| 2459996 | dish_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.595170 | -0.691650 | 1.096705 | -0.552008 | 0.446375 | 4.733016 | -0.803850 | 1.037007 | 0.5242 | 0.5235 | 0.3963 | nan | nan |
| 2459995 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.551943 | -1.029546 | 1.150128 | -1.328252 | 0.535695 | 3.929183 | -1.068386 | 0.597019 | 0.5182 | 0.5229 | 0.3838 | nan | nan |
| 2459994 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.387334 | -1.015127 | 1.116924 | -1.341640 | 0.541958 | -1.175687 | -1.433631 | -0.234665 | 0.5110 | 0.5150 | 0.3790 | nan | nan |
| 2459993 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.833997 | -0.782071 | 1.371262 | -1.330947 | 1.235905 | -0.696882 | -0.912549 | 0.624318 | 0.5009 | 0.5223 | 0.3796 | nan | nan |
| 2459991 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.396392 | -1.010032 | 1.277158 | -1.355399 | 1.008792 | -1.175376 | -1.217604 | -0.035882 | 0.5200 | 0.5155 | 0.3895 | nan | nan |
| 2459990 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.535848 | -0.747382 | 1.293983 | -1.246441 | 0.703426 | -1.028292 | -1.473123 | -0.251570 | 0.5194 | 0.5163 | 0.3845 | nan | nan |
| 2459989 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.438985 | -0.734207 | 1.363042 | -1.134936 | 0.580776 | -1.370732 | -1.370313 | -0.147061 | 0.5128 | 0.5143 | 0.3878 | nan | nan |
| 2459988 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.394106 | -0.726913 | 1.297062 | -1.310647 | 1.075661 | -1.042860 | -1.280351 | 0.158545 | 0.5140 | 0.5170 | 0.3771 | nan | nan |
| 2459987 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.354537 | -1.066452 | 1.148469 | -1.319811 | 0.161860 | 2.718339 | -1.772659 | 0.961175 | 0.5248 | 0.5272 | 0.3764 | nan | nan |
| 2459986 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.887144 | -0.925265 | 1.278816 | -1.415076 | 0.841037 | -0.871230 | -0.983534 | 3.704255 | 0.5375 | 0.5477 | 0.3512 | nan | nan |
| 2459985 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.474934 | -0.914751 | 1.048589 | -1.462238 | 0.321150 | -1.382029 | -1.783439 | -0.151880 | 0.5237 | 0.5268 | 0.3875 | nan | nan |
| 2459984 | dish_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.272658 | 0.007147 | 1.107066 | -0.674310 | 1.213715 | 10.996184 | -1.146852 | 0.342104 | 0.5357 | 0.5478 | 0.3746 | nan | nan |
| 2459983 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.300258 | -0.997648 | 1.171638 | -1.251683 | 1.135721 | -0.337448 | -0.334329 | 1.813258 | 0.5325 | 0.5430 | 0.3545 | nan | nan |
| 2459982 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.098025 | -0.988296 | 0.554955 | -1.158675 | -0.803865 | -0.705279 | -0.399340 | 1.587262 | 0.6402 | 0.6118 | 0.2923 | nan | nan |
| 2459981 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.780080 | -0.677695 | 1.415722 | -1.251633 | 1.149814 | -0.887355 | -1.215078 | -0.292981 | 0.5237 | 0.5248 | 0.3837 | nan | nan |
| 2459980 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.531389 | -0.886950 | 0.976233 | -1.564206 | 0.628084 | -0.919904 | -0.483787 | 1.646885 | 0.5838 | 0.5799 | 0.3116 | nan | nan |
| 2459979 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.606128 | -0.890663 | 1.043187 | -1.482612 | 0.687972 | -1.251991 | -1.616820 | -0.524733 | 0.5175 | 0.5220 | 0.3832 | nan | nan |
| 2459978 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.736066 | -0.866796 | 1.247230 | -1.396405 | 0.720913 | -1.097843 | -1.742438 | -0.314602 | 0.5165 | 0.5176 | 0.3936 | nan | nan |
| 2459977 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.039540 | -0.852509 | 0.986837 | -1.506558 | 0.725212 | -1.531993 | -1.693355 | -0.594858 | 0.4762 | 0.4774 | 0.3448 | nan | nan |
| 2459976 | dish_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.141119 | -0.985299 | 1.191452 | -1.388583 | 0.850513 | -0.948992 | -1.153896 | 0.066970 | 0.5286 | 0.5285 | 0.3782 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.005862 | 1.005862 | -0.824335 | 0.908134 | -1.217181 | -0.045500 | -1.029718 | -1.729023 | 0.561352 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.421698 | 1.421698 | -0.995988 | 1.177704 | -1.160338 | 0.446032 | -1.128413 | -2.341866 | 1.052290 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Variability | 4.733016 | 1.595170 | -0.691650 | 1.096705 | -0.552008 | 0.446375 | 4.733016 | -0.803850 | 1.037007 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Variability | 3.929183 | 1.551943 | -1.029546 | 1.150128 | -1.328252 | 0.535695 | 3.929183 | -1.068386 | 0.597019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.387334 | 1.387334 | -1.015127 | 1.116924 | -1.341640 | 0.541958 | -1.175687 | -1.433631 | -0.234665 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.833997 | 1.833997 | -0.782071 | 1.371262 | -1.330947 | 1.235905 | -0.696882 | -0.912549 | 0.624318 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.396392 | 1.396392 | -1.010032 | 1.277158 | -1.355399 | 1.008792 | -1.175376 | -1.217604 | -0.035882 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.535848 | -0.747382 | 1.535848 | -1.246441 | 1.293983 | -1.028292 | 0.703426 | -0.251570 | -1.473123 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.438985 | -0.734207 | 1.438985 | -1.134936 | 1.363042 | -1.370732 | 0.580776 | -0.147061 | -1.370313 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.394106 | -0.726913 | 1.394106 | -1.310647 | 1.297062 | -1.042860 | 1.075661 | 0.158545 | -1.280351 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Variability | 2.718339 | 1.354537 | -1.066452 | 1.148469 | -1.319811 | 0.161860 | 2.718339 | -1.772659 | 0.961175 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Discontinuties | 3.704255 | -0.925265 | 1.887144 | -1.415076 | 1.278816 | -0.871230 | 0.841037 | 3.704255 | -0.983534 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.474934 | -0.914751 | 1.474934 | -1.462238 | 1.048589 | -1.382029 | 0.321150 | -0.151880 | -1.783439 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Variability | 10.996184 | 2.272658 | 0.007147 | 1.107066 | -0.674310 | 1.213715 | 10.996184 | -1.146852 | 0.342104 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Discontinuties | 1.813258 | 1.300258 | -0.997648 | 1.171638 | -1.251683 | 1.135721 | -0.337448 | -0.334329 | 1.813258 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Discontinuties | 1.587262 | -0.098025 | -0.988296 | 0.554955 | -1.158675 | -0.803865 | -0.705279 | -0.399340 | 1.587262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Power | 1.415722 | -0.677695 | 0.780080 | -1.251633 | 1.415722 | -0.887355 | 1.149814 | -0.292981 | -1.215078 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | nn Temporal Discontinuties | 1.646885 | -0.886950 | 0.531389 | -1.564206 | 0.976233 | -0.919904 | 0.628084 | 1.646885 | -0.483787 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Power | 1.043187 | 0.606128 | -0.890663 | 1.043187 | -1.482612 | 0.687972 | -1.251991 | -1.616820 | -0.524733 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Power | 1.247230 | -0.866796 | 0.736066 | -1.396405 | 1.247230 | -1.097843 | 0.720913 | -0.314602 | -1.742438 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Shape | 1.039540 | 1.039540 | -0.852509 | 0.986837 | -1.506558 | 0.725212 | -1.531993 | -1.693355 | -0.594858 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | N09 | dish_ok | ee Power | 1.191452 | -0.985299 | 0.141119 | -1.388583 | 1.191452 | -0.948992 | 0.850513 | 0.066970 | -1.153896 |